Ontology-Based Recommendation of Editorial Products
Thiviyan Thanapalasingam, Francesco Osborne, Aliaksandr Birukou and, Enrico Motta

TL;DR
This paper presents an ontology-based recommender system that leverages a large-scale research taxonomy to assist publishers in selecting relevant scientific products for specific venues, improving over manual methods.
Contribution
The paper introduces the Smart Book Recommender, an innovative ontology-based system utilizing the Computer Science Ontology for precise, automated editorial product recommendations.
Findings
Effective recommendation accuracy confirmed by user evaluation.
Supports exploration of why items are recommended via interactive graph.
Enhances editorial decision-making with semantic analysis.
Abstract
Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR…
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